Abstract: To address the insufficient adaptability of traditional single-objective scheduling models for underground metal mines,a multi-objective scheduling optimization model for stoping equipment was constructed. The model takes the minimization of the makespan and total inter-operation interval time as the optimization objectives and incorporates the actual workingcondition characteristics of equipment operating states and resource requirements in ore mining,haulage,and hoisting. To reasonably transform the multi-objective scheduling problem into a solvable single-objective decision model,a multi-objective comprehensive weighting method based on the Analytic Hierarchy Process-Entropy Weight Method(AHP-EWM) was designed. By combining static weighting with dynamic weight adjustment,this method achieves precise weight configuration of multi-objective parameters. Taking the weighted single-objective comprehensive optimization model as the object to be solved,and considering the inherent shortcomings of traditional genetic algorithms,such as slow convergence and susceptibility to local optima,adaptive operators and a simulated annealing mechanism were introduced,and an Improved Genetic Simulated Annealing Algorithm(IGASA) was proposed. Case study results show that,compared with conventional scheduling methods,the proposed method reduces the makespan by 36. 54% and the total inter-operation interval time by 38. 13%,indicating significantly improved scheduling performance. Furthermore,to intuitively apply the obtained scheduling scheme to production management,a process architecture for a production scheduling digital twin system was proposed,which supports dynamic simulation and real-time monitoring of scheduling plans,thereby forming a complete technical chain of " weight configuration-model solving-visualization presentation" and providing a theoretical basis and technical support for constructing intelligent scheduling systems for underground mines.